Caseau, Y. GTES : A method of game simulation and learning for systems of agents. (GTES : une méthode de simulation par jeux et apprentissage pour l’analyse des systèmes d’acteurs.) (French) Zbl 1187.91027 RAIRO, Oper. Res. 43, No. 4, 437-462 (2009). Summary: This paper proposes an approach towards modeling an actor system, especially suited to describe a company’s organization, based on game theory and learning-based (evolutionary) local optimization. This method relies on the combination of three techniques: sampling for simulation (Monte-Carlo), game theory as far as the search for equilibrium is concerned and heuristic local search methods, such as genetic algorithms. This combination is not original as such, although it is rarely used with the full combined expressive power of this array of techniques. Our contribution with this paper is twofold. On the one hand we propose a model which is a natural framework for the collaboration between these three techniques. On the other hand, we use genetic algorithms to extend the search of Nash equilibrium, obtained as fixed-points of an iterative transformation. This remains a simulation tool, not intended to solve problems but to validate a given model and to study its properties. Cited in 1 Document MSC: 91A26 Rationality and learning in game theory 91A80 Applications of game theory 68T05 Learning and adaptive systems in artificial intelligence 65C05 Monte Carlo methods 90C59 Approximation methods and heuristics in mathematical programming Keywords:simulation; learning; game theory; genetic algorithms; enterprise organization Software:OPEN DESIRE PDFBibTeX XMLCite \textit{Y. Caseau}, RAIRO, Oper. Res. 43, No. 4, 437--462 (2009; Zbl 1187.91027) Full Text: DOI EuDML References: [1] E. Aarts and J.K. Lenstra, Local search in combinatorial optimisation. Wiley (1993). [2] R. Axelrod, The complexity of cooperation-agent-based models of competitions and cooperation. Princeton University Press (1997). [3] Y. Caseau, G. Silverstein and F. Laburthe, Learning hybrid algorithms for vehicle routing problems. Theory Pract. Log. Program.1 (2001) 779-806. Zbl1066.68536 · Zbl 1066.68536 [4] G. Donnadieu and M. Karsky, La systémique, penser et agir dans la complexité. Éditions Liaisons (2002). [5] J. Dréo, A. Petrowski, P. Siarry and E. Taillard, Métaheuristiques pour l’optimisation difficile. Eyrolles, Paris (2003). [6] R. Duncan Luce and H. Raiffa, Games and decisions - Introduction and critical survey. Dover Publications, New York (1957). Zbl0084.15704 · Zbl 0084.15704 [7] J. Ferber, Les Systèmes multi-agents : vers une intelligence collective. Dunod, Paris (2007). · Zbl 0871.68023 [8] J. Forrester, Principles of systems. System Dynamics Series, Pegasus Communications, Waltham (1971). [9] R. Gibbons, Game theory for applied economists. Princeton University Press (1992). · Zbl 0759.90106 [10] S. Jørgensen, M. Quincampoix and T. Vincent (Eds.) Advances in dynamic game theory: numerical methods, algorithms, and applications to ecology and economics (Annals of the International Society of Dynamic Games). Birkhauser, Boston (2007). · Zbl 1113.91004 [11] G.A. Korn, Advanced dynamic-system simulation: model-replication techniques and Monte Carlo simulation. Wiley Interscience (2007). [12] M. Mongeau, Introduction à l’optimisation globale. Bulletin ROADEF N^\circ 19 (2007). [13] R. Nelson and S. Winter, An evolutionary theory of economic change. Belknap, Harvard (1982). [14] N. Nissan, T. Roughgarden, E. Tardos and V.V. Vazirani, Algorithmic game theory. Cambridge University Press (2007). · Zbl 1130.91005 [15] B. Slantchev, Game theory: repeated Games. University of California - San Diego. bslantch/courses/gt/07-repeated-games.pdf (2004). URIhttp://polisci.ucsd.edu/ [16] J. Sterman, Business dynamics - System thinking and modeling for a complex world. McGraw Hill (2001). [17] J. Watson, Strategy - An introduction to game theory. Norton (2002). This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.